JP7116007B2 - Track irregularity prediction method and track irregularity prediction system - Google Patents

Description

The present invention relates to a track irregularity prediction method and track irregularity prediction system for predicting track irregularity.

Track irregularity has been measured in the past as a method of predicting future track irregularity from track irregularity measurement data on railroad tracks. A method of predicting future track irregularity by data processing based on historical data and a method of reflecting newly obtained track irregularity data to improve the accuracy of prediction are known.

Here, the track displacement is the elevation displacement, which is the displacement in the vertical direction of the rail, the track displacement, which is the displacement in the width direction (horizontal direction) of the rail, and the difference from the set value of the interval between the rails on both the left and right sides. Gauge displacement, level displacement that is the difference from the set value of the height difference between the left and right rails, and flatness displacement that is the level difference between two points separated by a certain distance can be exemplified. can.

In Japanese Patent Application Laid-Open No. 2018-76724 (Patent Document 1), in a sequential update prediction determination method for time-series data obtained by measuring a predetermined track inspection section, based on newly acquired time-series data There is disclosed a technique of calculating a posterior distribution obtained by calculating the posterior distribution, and comparing the rate of change of the posterior distribution result one or more times before with the rate of change of the posterior distribution based on newly acquired data.

Japanese Patent Laying-Open No. 2018-76681 (Patent Document 2) discloses a track maintenance management method in which a step of predicting future changes in track irregularity based on track irregularity history data, and a step in which the predicted track irregularity exceeds a reference value. A process of selecting locations that exceed the reference value as maintenance targets, a process of judging the risk of locations where the predicted track irregularity does not exceed the reference value, and if the judged risk exceeds the threshold, the predicted track irregularity reaches the reference value. and a step of selecting a location that does not exceed , as a maintenance target location.

Japanese Patent Application Laid-Open No. 2017-110375 (Patent Document 3) describes a track correction procedure generation device in which a measurement data acquisition unit that acquires track measurement data, and a running simulation that simulates track running by a vehicle, are combined with the measurement data and the track. a simulation execution unit that performs a simulation based on the speed determined according to the position of the vehicle in the vehicle; Techniques are disclosed.

JP 2018-76724 A JP 2018-76681 A JP 2017-110375 A

In the conventional track irregularity prediction method, when sufficient history data of track irregularity acquired in the past is accumulated, the track irregularity is predicted based on the accumulated data. However, it is difficult to predict track irregularity with high accuracy when sufficient track irregularity history data is not accumulated.

The present invention has been made to solve the problems of the prior art as described above. An object of the present invention is to provide a track irregularity prediction method capable of predicting track irregularity with high accuracy.

A brief outline of typical inventions disclosed in the present application is as follows.

A track irregularity prediction method as one aspect of the present invention is a track irregularity prediction method for predicting track irregularity in each of a plurality of sections of a track including a plurality of sections arranged along the length direction of the track. The track irregularity prediction method measures track irregularity at a plurality of different measurement positions along the length direction in each of a plurality of sections, and measures a plurality of track irregularities measured at each of the plurality of measurement positions. (a) step of calculating a first index representing the variation of values; and (b) recalculating the No. 1 index. Further, in each of the plurality of sections, the track irregularity prediction method includes the first calculated value of the first index calculated in step (a) and the second calculated value of the first index calculated in step (b). (c) step of calculating, based on the calculated value, a second index representing the degree of change in track irregularity before the track irregularity is measured in step (b); and the third calculated values of the plurality of second indices calculated in each of the plurality of sections in step (c), the ratio between the first index and the second index and (d) a step of calculating a regression equation indicating the relationship. Further, the track irregularity prediction method calculates a first predicted value of the second index based on the first calculated value and the regression equation in each of the plurality of sections, and calculates the calculated first predicted value and , the third calculated value, and the (e) step of calculating the first correction coefficient indicating the relationship between the second calculated value, the regression equation, and the (e) step in each of the plurality of intervals (f) calculating a second predicted value of a third index representing the degree of change in track irregularity after the track irregularity is measured in step (b), based on the fourth calculated value of the first correction coefficient; and a step.

Further, as another aspect, in step (e), in each of the plurality of intervals, the first predicted value is calculated by substituting the first calculated value as the first index into the regression equation, and the calculated first A first correction coefficient, which is a ratio of the third calculated value to one predicted value, may be calculated.

Further, as another aspect, in step (f), in each of the plurality of intervals, a third predicted value of the second index is calculated by substituting the second calculated value as the first index into the regression equation, The second predicted value may be calculated by multiplying the calculated third predicted value by the first correction coefficient.

Further, as another aspect of the track irregularity prediction method, after measuring the track irregularity in the step (b), the track irregularity is measured again at a plurality of measurement positions in each of the plurality of sections, and the first index is recalculated, and in each of the plurality of sections, based on the second calculated value and the fifth calculated value of the first index calculated in step (g), step (g) and (h) calculating a fourth index representing the degree of change in track irregularity before the track irregularity is measured in step (h). Further, the track irregularity prediction method calculates the relationship between the third predicted value, the sixth calculated value of the fourth index calculated in step (h), and the first correction coefficient in each of the plurality of sections. (i) step of calculating the second correction coefficient shown, in each of the plurality of sections, the fifth calculated value, the regression equation, the fourth calculated value, and the second correction coefficient calculated in the (i) step and a step (j) of calculating a fourth predicted value of the fifth index representing the degree of change in track irregularity after the track irregularity is measured in step (g), based on the seventh calculated value of may have.

Further, as another aspect, in step (i), in each of the plurality of sections, a second correction coefficient is calculated, which is the ratio of the sixth calculated value to the product of the third predicted value and the first correction coefficient. You may

Further, as another aspect, in step (e), in each of the plurality of intervals, the first predicted value is calculated by substituting the first calculated value as the first index into the regression equation, and the calculated first A first correction coefficient may be calculated as a difference between the third calculated value and the one predicted value.

A track irregularity prediction method as one aspect of the present invention is a track irregularity prediction method for predicting track irregularity in each of a plurality of sections of a track including a plurality of sections arranged along the length direction of the track. The track irregularity prediction method measures track irregularity at a plurality of different measurement positions along the length direction in each of a plurality of sections, and measures a plurality of track irregularities measured at each of the plurality of measurement positions. (a) step of calculating, based on the values, a first index representing variation in measured values of track irregularities and a second index representing wheel load or rail pressure in each of a plurality of sections; (b) step of measuring the track irregularity again at a plurality of measurement positions in each of the plurality of sections after measuring the track irregularity in step a), and recalculating the first index and the second index; have. Further, in each of the plurality of sections, the track irregularity prediction method includes the first calculated value of the first index calculated in step (a) and the second calculated value of the first index calculated in step (b). (c) calculating a third index representing the degree of change in track irregularity before the track irregularity is measured in step (b) based on the calculated value; and (a) calculating a plurality of sections in step (a). and the third calculated values of the plurality of third indices calculated in each of the plurality of sections in step (c). and (d) calculating a regression equation representing the relationship between the second index and the third index. Further, the track irregularity prediction method calculates the first predicted value of the third index based on the first index calculated value and the regression equation in each of the plurality of sections, and calculates the calculated first predicted value and the third calculated value, and the second index calculated value of the second index calculated in step (b) in each of the plurality of sections. , the regression equation, and the fourth calculated value of the first correction coefficient calculated in step (e), the degree of change in track irregularity after measuring the track irregularity in step (b) is calculated. and (f) calculating a second predicted value of the fourth index to represent.

A track irregularity prediction system as one aspect of the present invention is a track irregularity prediction system that predicts track irregularity in each of a plurality of sections of a track including a plurality of sections arranged along the length direction of the track. The track irregularity prediction system measures track irregularity at a plurality of measurement positions different from each other along the length direction in each of a plurality of sections, and measures a plurality of track irregularities measured at each of the plurality of measurement positions. A first calculation unit that calculates a first index representing variation in values; and a second calculation unit that recalculates the No. 1 index. In addition, the track irregularity prediction system has a first calculated value of the first index calculated by the first calculator and a second calculated value of the first index calculated by the second calculator in each of the plurality of sections. and a third calculator that calculates a second index representing the degree of change in track irregularity before the track irregularity is measured by the second calculator, based on and the third calculated values of the plurality of second indices calculated in each of the plurality of sections by the third calculation unit. and a fourth calculation unit that calculates the regression equation. Further, the track irregularity prediction system calculates a first predicted value of the second index based on the first calculated value and the regression equation in each of the plurality of sections, and calculates the calculated first predicted value and , a third calculated value, and a fifth calculation unit that calculates a first correction coefficient indicating the relationship between the second calculated value, the regression equation, and the fifth calculated by the fifth calculation unit in each of the plurality of sections; a fourth calculated value of the 1 correction coefficient; and have

Further, as another aspect, the fifth calculation unit calculates the first predicted value by substituting the first calculated value as the first index into the regression equation in each of the plurality of intervals, and calculates the calculated first A first correction coefficient, which is a ratio of the third calculated value to one predicted value, may be calculated.

Further, as another aspect, the sixth calculation unit calculates a third predicted value of the second index by substituting the second calculated value as the first index into the regression equation in each of the plurality of intervals, The second predicted value may be calculated by multiplying the calculated third predicted value by the first correction coefficient.

Further, as another aspect, the track irregularity prediction system measures the track irregularity again at a plurality of measurement positions in each of the plurality of sections after the track irregularity is measured by the second calculator, and calculates the first index. and the seventh calculation unit based on the second calculated value and the fifth calculated value of the first index calculated by the seventh calculation unit in each of the plurality of sections. and an eighth calculator that calculates a fourth index representing a degree of change in the track irregularity before the track irregularity is measured. Further, the track irregularity prediction system indicates the relationship between the third predicted value, the sixth calculated value of the fourth index calculated by the eighth calculator, and the first correction coefficient in each of the plurality of sections. a ninth calculation unit that calculates the second correction coefficient; and a fifth calculation value, a regression equation, a fourth calculation value, and the second correction coefficient calculated by the ninth calculation unit in each of the plurality of sections. and a tenth calculator that calculates a fourth predicted value of the fifth index representing the degree of change in track irregularity after the track irregularity is measured by the seventh calculator, based on the calculated values. may

Further, as another aspect, the ninth calculation unit calculates the second correction coefficient, which is the ratio of the sixth calculated value to the product of the third predicted value and the first correction coefficient, in each of the plurality of sections. You may

Further, as another aspect, the fifth calculation unit calculates the first predicted value by substituting the first calculated value as the first index into the regression equation in each of the plurality of intervals, and calculates the calculated first A first correction coefficient may be calculated as a difference between the third calculated value and the one predicted value.

By applying one aspect of the present invention, in a track irregularity prediction method for predicting track irregularity, future track irregularity can be predicted with high accuracy even when track irregularity history data is small and insufficient.

1 is a block diagram showing the configuration of a track irregularity prediction system according to Embodiment 1; FIG. FIG. 4 is a flowchart showing some steps of the track irregularity prediction method of Embodiment 1; FIG. 4 is a diagram for explaining the first measurement of track irregularity by the track irregularity prediction method of the first embodiment; FIG. 7 is a diagram for explaining second measurement of track irregularity by the track irregularity prediction method of the first embodiment; 4 is a diagram for explaining calculation of a regression equation by the track irregularity prediction method of Embodiment 1; FIG. 4 is a diagram for explaining calculation of a correction coefficient by the track irregularity prediction method of Embodiment 1; FIG. FIG. 7 is a diagram for explaining the third measurement of track irregularity by the track irregularity prediction method of the first embodiment; 4 is a diagram for explaining calculation of a correction coefficient by the track irregularity prediction method of Embodiment 1; FIG. FIG. 5 is a diagram for explaining calculation of a correction coefficient by a track irregularity prediction method according to a modified example of Embodiment 1; FIG. 10 is a diagram for explaining calculation of a regression equation by the track irregularity prediction method of Embodiment 2; FIG. 9 is a diagram for explaining calculation of a correction coefficient by the track irregularity prediction method of Embodiment 2; FIG. 9 is a diagram for explaining calculation of a correction coefficient by the track irregularity prediction method of Embodiment 2;

Each embodiment of the present invention will be described below with reference to the drawings.

It should be noted that the disclosure is merely an example, and those skilled in the art will naturally include within the scope of the present invention any appropriate modifications that can be easily conceived while maintaining the gist of the invention. In addition, in order to make the description clearer, the drawings may schematically show the width, thickness, shape, etc. of each part compared to the embodiment, but this is only an example, and the interpretation of the present invention is not limited. It is not limited.

In addition, in this specification and each figure, the same reference numerals may be given to the same elements as those described above with respect to the previous figures, and detailed description thereof may be omitted as appropriate.

Furthermore, in the drawings used in the embodiments, hatching may be omitted even in cross-sectional views in order to make the drawings easier to see. Also, even a plan view may be hatched to make the drawing easier to see.

In the following embodiments, when a range is indicated as A to B, it indicates A or more and B or less, unless otherwise specified.

(Embodiment 1)
<Track irregularity prediction system and track irregularity prediction method>
First, a track irregularity prediction system and a track irregularity prediction method according to Embodiment 1 will be described. The track irregularity prediction system of Embodiment 1 is a track irregularity prediction system that predicts track irregularity in each of a plurality of sections of a track that includes a plurality of sections arranged along the length direction of the track. The track irregularity prediction method of Embodiment 1 is a track irregularity prediction method for predicting track irregularity in each of a plurality of sections of a track including a plurality of sections arranged along the length direction of the track. It is a track irregularity prediction method using the track irregularity prediction system of form 1.

FIG. 1 is a block diagram showing the configuration of a track irregularity prediction system according to Embodiment 1. FIG. FIG. 2 is a flowchart showing some steps of the track irregularity prediction method according to the first embodiment. FIG. 3 is a diagram for explaining the first measurement of track irregularity by the track irregularity prediction method of the first embodiment. FIG. 4 is a diagram for explaining the second track irregularity measurement by the track irregularity prediction method of the first embodiment. FIG. 5 is a diagram for explaining calculation of a regression equation by the track irregularity prediction method of the first embodiment. FIG. 6 is a diagram for explaining calculation of a correction coefficient by the track irregularity prediction method according to the first embodiment.

As shown in FIG. 1, the track irregularity prediction system 10 of the first embodiment includes a first calculator 11, a second calculator 12, a third calculator 13, a fourth calculator 14, a fifth It has a calculator 15 , a sixth calculator 16 , a seventh calculator 17 , an eighth calculator 18 , a ninth calculator 19 , and a tenth calculator 20 . A calculation unit 21 is configured by the first calculation unit 11 to the tenth calculation unit 20 . Further, the track irregularity prediction system of Embodiment 1 may include a measuring section 22 that measures track irregularities, and a control section 23 that controls operations of the calculating section 21 and the measuring section 22 . As the first calculation unit 11 to the tenth calculation unit 20, that is, the calculation unit 21 and the control unit 23, for example, a computer that executes a program for causing each calculation unit to perform a certain operation can be used.

The measuring unit 22 is provided in, for example, a track inspection car, and measures track displacement, which is the displacement of the track, when the track inspection car travels on the track. In addition, as described above, the track displacement is determined from the set value of the track displacement, which is the vertical displacement of the rail, the alignment displacement, which is the displacement of the rail in the width direction (horizontal direction), and the distance between the rails on both the left and right sides. Gauge displacement, which is the difference between the rails, level displacement, which is the difference from the set value of the height difference between the left and right rails, and flatness displacement, which is the level difference between two points separated by a certain distance. can be exemplified. As the measurement unit 22, various types of measurement devices that can appropriately measure various track displacements can be used.

In the track irregularity prediction method of Embodiment 1, first, as shown in FIG. In each of a plurality of sections SC in the track RA, the track displacement TR is measured by the measuring unit 22 (see FIG. 1) at a plurality of measurement positions MP different from each other along the length direction of the track RA. An index IN1 representing the dispersion of the measured values of the track displacement TR measured at each of the above is calculated (step S11 in FIG. 2).

Preferably, the index IN1 can be the standard deviation or the maximum value of the measured values of the track irregularities TR, but the case where the standard deviation is used will be described below as an example. In such a case, as shown in FIG. 2, in step S11, the standard deviation of the first track displacement TR is calculated. In the specification of the present application, the first track displacement TR means the track displacement TR of the first measurement, and the same applies to the second and subsequent measurements.

FIG. 3 shows the measured values obtained by measuring the track displacement TR for the first time in step S11. In FIG. 3, five sections SC are displayed as section SC1, section SC2, section SC3, section SC4, and section SC5 as an example. Further, in FIG. 3, the first track displacement TR is indicated by a solid line as the track displacement TR1.

When using the standard deviation of the measured values of track irregularities TR as the index IN1, as shown in FIG. 3, as the calculated value CV1 of the index IN1, A calculated value of the standard deviation σ ₁ of a plurality of measured values of track displacement TR can be used. Then, the calculated value CV1 of the index IN1 is used as the standard deviation of the first track displacement TR, and can be regarded as the first track displacement TR.

In addition, below, the standard deviation of the track irregularity TR generalized without specifying how many times the track irregularity TR is measured is defined as the standard deviation σ.

In the track irregularity prediction method of the first embodiment, after the first calculator 11 measures the track irregularity TR with the measuring unit 22 in step S11, the second calculator 12 (see FIG. 1) performs the following operations as shown in FIG. Thus, in each of the plurality of sections SC, the track displacement TR is measured again at the plurality of measurement positions MP by the measuring unit 22, and the index IN1 is calculated again (step S12 in FIG. 2).

As described above, when the standard deviation of a plurality of measured values of track irregularity TR is used as the index IN1, the standard deviation of the second track irregularity TR is calculated in step S12 as shown in FIG. . After the first measurement of the track displacement TR, the second measurement of the track displacement TR is performed after a predetermined period of time has passed. For example, after a period of several months to a year, a second track displacement TR can be measured.

In FIG. 4, as in FIG. 3, five sections SC are indicated as section SC1, section SC2, section SC3, section SC4 and section SC5 as an example. In FIG. 4, for the sake of easy understanding, the track displacement TR of the second measurement is indicated by a solid line as the track displacement TR2, and the track displacement TR of the first measurement is indicated by a broken line as the track displacement TR1.

As described above, when the standard deviation of a plurality of measured values of track irregularity TR is used as index IN1, as shown in FIG. A calculated value of the standard deviation σ ₂ of a plurality of measured values of track displacement TR measured by each MP can be used. Then, the calculated value CV2 of the index IN1 is used as the standard deviation of the second track displacement TR, and can be regarded as the second track displacement TR.

In the track irregularity prediction method of the first embodiment, next, as shown in FIG. 4, the third calculator 13 (see FIG. 1) performs the first calculator 11 and the calculated value CV2 of the index IN1 calculated by the second calculation unit 12 in step S12, the first calculation unit 11 calculates the measurement unit in step S11. After the track displacement TR is measured by 22 and before the track displacement TR is measured by the second calculation unit 12 by the measurement unit 22 in step S12, an index IN2 representing the degree of change in the track displacement TR is calculated ( Step S13 in FIG. 2).

As described above, when the standard deviation of a plurality of track displacement TR measurements is used as the index IN1, in step S13, the difference between the first and second standard deviations is calculated as shown in FIG. . Further, as the calculated value CV3 of the index IN2, the calculated value of the difference _Δσ12 between the first and second standard deviations can be used. That is, the difference between the calculated value CV2 of the index IN1 and the calculated value CV1 of the index IN1 can be used as the index IN2. Also, the difference Δσ ₁₂ between the first and second standard deviations is calculated by the following formula (Equation 1).

In the following description, the standard deviation difference between two consecutive measurements of the track displacement TR is defined as the standard deviation difference Δσ, which is generalized without specifying the number of measurements of the track displacement TR.

In the track irregularity prediction method of the first embodiment, next, as shown in FIG. 5, the fourth calculator 14 (see FIG. 1) causes the first calculator 11 to and a plurality of calculated values CV3 of the index IN2 calculated in _each of the plurality of sections SC by the second calculator 12 in step S12. (Difference of standard deviations Δσ ₁₂ ), and a regression equation RE1 representing the relationship between the index IN1 and the index IN2 is calculated (step S14 in FIG. 2). FIG. 5 schematically shows a graph in which the horizontal axis represents the standard deviation σ of the track displacement TR and the vertical axis represents the difference Δσ of the standard deviation.

As described above, when the standard deviation of _a plurality of measured track irregularities TR is used as the index _IN1 , as shown in FIG. RE1 will be calculated.

As shown in FIG. 5, a graph in which the horizontal axis is the standard deviation σ of the track irregularity TR and the vertical axis is the difference Δσ in the standard deviation shows the calculated values CV1 ( Plot the set data of the standard deviation σ ₁ ) and the calculated value CV3 of the index IN2 (the standard deviation difference Δσ ₁₂ ). Then, a regression calculation is performed using the regression formula RE1 represented by the following formula (Formula 2) or the following formula (Formula 3) as an example of the following formula (Formula 2), for example, so that the correlation coefficient is maximized. Determine the appropriate values of a and b. With such a method, the regression equation RE1 can be calculated.

In the track irregularity prediction method of the first embodiment, next, as shown in FIG. 6, the fifth calculator 15 (see FIG. 1) calculates the first calculator ₁₁ and the regression equation RE1, the predicted value PV1 of the index IN2 is calculated, and the calculated predicted value PV1 of the index IN2 and the index A correction coefficient CF1 indicating the relationship between the calculated value CV3 of IN2 is calculated (step S15 in FIG. 2). As in FIG. 5, FIG. 6 schematically shows a graph in which the horizontal axis represents the standard deviation σ of the track displacement TR and the vertical axis represents the difference Δσ between the standard deviations.

As described above, when the standard deviation of a plurality of measured values of track irregularity TR is used as index _IN1 , in step S15, as shown in FIG. A coefficient CF1 is calculated.

Preferably, as shown in FIG. 6, in step S15, the fifth calculator 15 predicts the index IN2 by substituting the calculated value CV1 as the index IN1 into the regression equation RE1 in each of the plurality of sections SC. Calculate the value PV1. Preferably, as shown in FIG. 6, in step S15, the fifth calculator 15 calculates the ratio of the calculated value CV3 of the index IN2 to the calculated predicted value PV1 of the index IN2 in each of the plurality of sections SC. A correction coefficient CF1 is calculated. Further, when the correction coefficient CF1 is expressed as _a correction coefficient γ1, the correction coefficient γ1 is expressed by the following formula (Equation ₄ ).

Here, the numerator on the right side of the above equation (Equation 4) represents the calculated value CV3 of the index IN2 (the difference in standard deviation Δσ ₁₂ ), and the denominator on the right side of the above equation (Equation 4) is: It represents the predicted value PV1 of the index IN2.

Further, when the regression formula RE1 is represented by the above formula (Equation 3), the calculated value CV3 (the difference in standard deviation Δσ ₁₂ ) of the index IN2 must satisfy the correction formula CE1 represented by the following formula (Equation 5). become. In FIG. 6, the regression equation RE1 is indicated by a solid line, and the correction equation CE1 is indicated by a broken line.

In the first embodiment, in step S15, a correction coefficient is calculated from a predicted value of track irregularity based on a basic model (regression formula) and an actual measured value of track irregularity. As shown in FIG. 6, in the first embodiment, as an example of a calculation method, a correction coefficient is calculated by dividing an actual measured value of track irregularity by a predicted value of track irregularity based on the basic model. showing. Such a method makes it possible to easily calculate the correction factor.

That is, in the first embodiment, in steps S11 to S15, a basic model for predicting track irregularity is constructed in a relatively long section, and a correction coefficient is applied to the track irregularity predicted by the basic model for each short section. _Multiply by γ1. The basic model, for example, calculates the progress of the standard deviation of the track irregularity from the initial standard deviation of the track irregularity or the wheel load. As shown in FIGS. 3 and 4, the initial track irregularity and progress of track irregularity, which are input values, are calculated from the measured track irregularities, and as shown in FIG. etc. to estimate the approximated curve and construct the basic model.

In the track irregularity prediction method of the first embodiment, next, as shown in FIG. 6, the sixth calculator 16 (see FIG. 1) performs the second calculator 12, the regression equation RE1, and the calculated value CV4 of the correction coefficient CF1 calculated by the fifth calculator 15 in step S15, the second The calculator 12 calculates a predicted value PV2 of the index IN3 representing the degree of change in the track irregularity TR after the track irregularity TR is measured by the measuring unit 22 (step S16 in FIG. 2).

As described above, when the standard deviation of a plurality of measured values of track irregularity TR is used as index IN1, as shown in FIG. Predict the difference. Further, if the predicted value PV2 of the index IN3 is expressed as the predicted value Δσ ₂ ^* of the difference between the second and third standard deviations, the predicted value PV2 of the index IN3 is expressed by the following equation (Equation 6).

Here, assuming that the factor on the left side of the right side of the above equation (Formula 6) is the predicted value PV3 of the index IN2, the predicted value PV3 of the index IN2 is added to the regression equation RE1 as the index IN1 to calculate the calculated value CV2 (standard It is calculated by substituting the deviation σ ₂ ). That is, preferably, in step S16, the sixth calculator 16 calculates the predicted value PV3 of the index IN2 by substituting the calculated value CV2 as the index IN1 into the regression equation RE1 in each of the plurality of sections SC. , the predicted value PV2 of the index IN3 is calculated by multiplying the calculated predicted value PV3 of the index IN2 by the correction coefficient CF1. With such a method, the predicted value PV2 of the index IN3 can be easily calculated.

In the conventional track irregularity prediction method, when sufficient history data of track irregularity acquired in the past is accumulated, the track irregularity is predicted based on the accumulated data. However, if sufficient track irregularity history data is not accumulated, or if track irregularity history data is sufficiently accumulated but conditions such as track structure, rolling stock, or driving conditions change, high-accuracy It was difficult to predict track irregularity.

Further, in the conventional method for creating a track maintenance plan, simulation is used to predict acceleration or rolling stock, and the prediction is used in the track maintenance plan. At this time, it was necessary to correct the track irregularity (the amount of unevenness of the road surface) by comparing the design value and the actual measurement value.

On the other hand, in the track irregularity prediction method using the track irregularity prediction system of Embodiment 1, the calculated value CV1 of the index IN1 representing the dispersion of the track irregularity TR measured the first time and the track irregularity measured the second time An index IN2 representing the degree of change in track displacement TR before the second measurement is calculated based on a calculated value CV2 of the index IN1 representing the variation in TR, and a regression formula showing the relationship between the index IN1 and the index IN2. Calculate RE1. Further, in the track irregularity prediction method using the track irregularity prediction system of Embodiment 1, correction indicating the relationship between the predicted value PV1 of the index IN2 calculated based on the regression equation RE1 and the calculated value CV3 of the index IN2 Based on the calculated value CV4 of the coefficient CF1, an index IN3 representing the degree of change in the track displacement TR after the second measurement is predicted.

Even if there is little track irregularity history data, it is possible to check the trend of track irregularity changes within a certain long section if several measurements of track irregularity have been obtained. be. Therefore, in the track irregularity prediction method using the track irregularity prediction system of Embodiment 1, first, a basic model (regression formula) for predicting the trend of track irregularity change within a relatively long section is constructed. roughly predict the change in track irregularity in Next, the model is divided into shorter sections, and a different correction coefficient is set for each shorter section to correct the calculation result using the basic model. The basic model calculates, for example, the initial standard deviation of track irregularity or the progress of the standard deviation of track irregularity from the wheel load.

Therefore, in the track irregularity prediction method using the track irregularity prediction system of the first embodiment, it is sufficient to have the measured values of the track irregularity measured in the past two measurements, and it is not necessary to perform measurements many more times. do not have. Therefore, even if the history data of the track irregularity is small and insufficient, the future track irregularity can be predicted with high accuracy. In addition, even if a sufficient amount of track irregularity history data has been accumulated, it is difficult to predict track irregularity with high accuracy when conditions such as track structure, rolling stock, or driving conditions change. According to Mode 1, even in such a case, future track irregularity can be predicted with high accuracy. In the first embodiment, the advance of the standard deviation of the track irregularity is calculated from the initial standard deviation of the track irregularity, but in the second embodiment, the advance of the standard deviation of the irregular track is calculated from the initial wheel load. .

Further, in the track irregularity prediction method using the track irregularity prediction system of the first embodiment, a model for predicting future track irregularity is created from the track irregularity measurement results, and the past two track irregularity measurements, By comparing the predicted value of the track irregularity for one time by the created model, the predicted value of the subsequent track irregularity is further corrected. Therefore, it is possible to predict future track irregularity with high accuracy without performing a simulation.

Further, according to the track irregularity prediction method using the track irregularity prediction system of the first embodiment, the life cycle cost can be calculated by calculating the predicted value of the track maintenance cycle from the calculated predicted value of the track irregularity. It is also possible to

Further, in the track irregularity prediction method using the track irregularity prediction system of the first embodiment, the correction coefficient can be set according to the characteristics of each short section or the trend of track irregularity change.

Further, in the track irregularity prediction method using the track irregularity prediction system of Embodiment 1, the correction coefficient can be updated each time measurement data of track irregularity is added. The updating of the correction coefficients will be described below.

FIG. 7 is a diagram for explaining the third measurement of track irregularity by the track irregularity prediction method of the first embodiment. FIG. 8 is a diagram for explaining calculation of a correction coefficient by the track irregularity prediction method according to the first embodiment.

Preferably, after the second calculation unit 12 measures the track displacement TR by the measurement unit 22 in step S12, the seventh calculation unit 17 (see FIG. 1) calculates a plurality of sections SC as shown in FIG. At each of the measurement positions MP, the track displacement TR is measured again by the measuring unit 22, and the index IN1 is calculated again (step S17 in FIG. 2).

When the standard deviation of a plurality of measured values of track displacement TR is used as index IN1, as shown in FIG. 2, in step S17, the standard deviation of track displacement TR for the third time is calculated. Further, after the second measurement of the track displacement TR, the third measurement of the track displacement TR is performed after a predetermined period of time has passed. After the elapse of a period equal to the period until the measurement of the track displacement TR for the third time, the measurement of the track displacement TR can be performed for the third time.

In FIG. 7, as in FIG. 3, as an example, the five sections SC are indicated as section SC1, section SC2, section SC3, section SC4 and section SC5. Also, in FIG. 7, for ease of understanding, the third track displacement TR is indicated by a solid line as a track displacement TR3, and the second track displacement TR is indicated by a broken line as a track displacement TR2.

As described above, when the standard deviation of a plurality of measured values of track irregularity TR is used as index IN1, as shown in FIG. A calculated value of the standard deviation _σ3 of a plurality of measured values of the track displacement TR measured by each of the MPs can be used. Then, the calculated value CV5 of the index IN1 is used as the standard deviation of the third track displacement TR, and can be regarded as the third track displacement TR.

In the track irregularity prediction method of Embodiment 1, next, as shown in FIG. 12, and the calculated value CV5 of the index IN1 calculated by the seventh calculation unit 17 in step S17, the second calculation unit 12 calculates the measurement unit in step S12. After the track displacement TR is measured by 22 and before the track displacement TR is measured by the measurement unit 22 by the seventh calculation unit 17 in step S17, an index IN4 representing the degree of change in the track displacement TR is calculated ( Step S18 in FIG. 2).

As described above, when the standard deviation of a plurality of measured values of track irregularity TR is used as the index IN1, as shown in FIG. . As the calculated value CV6 of the index IN4, the calculated value of the difference _Δσ23 between the second and third standard deviations can be used. Also, the difference Δσ ₂₃ between the second and third standard deviations is calculated by the following equation (Equation 7).

In the track irregularity prediction method of the first embodiment, next, as shown in FIG. 8, the ninth calculator 19 (see FIG. 1) calculates the predicted value PV3 of the index IN2 in each of the plurality of sections SC, A correction coefficient CF2 indicating the relationship between the calculated value CV6 of the index IN4 calculated by the eighth calculator 18 in step S18 and the correction coefficient CF1 is calculated (step S19 in FIG. 2). As in FIG. 5, FIG. 8 schematically shows a graph in which the horizontal axis represents the standard deviation σ of the track displacement TR and the vertical axis represents the difference Δσ between the standard deviations.

As described above, when the standard deviation of the measured values of track irregularities TR is used as the index _IN1 , as shown in FIG. A coefficient CF2 is calculated.

Preferably, as shown in FIG. 8, in step S19, the ninth calculator 19 calculates the calculated value of the index IN4 with respect to the product of the predicted value PV3 of the index IN2 and the correction coefficient CF1 in each of the plurality of sections SC. A correction factor CF2, which is a ratio of CV6, is calculated. Further, when the correction coefficient CF2 is expressed as a correction coefficient γ2, the correction coefficient γ2 is expressed by the following _formula (Equation ₈ ).

Here, the numerator on the right side of the above equation (Equation 8) represents the calculated value CV6 (difference of standard deviation Δσ ₂₃ ) of the index IN2, and the denominator on the right side of the above equation (Equation 8) is The factor on the left represents the predicted value PV3 of the index IN2 and is calculated by substituting the calculated value CV2 (standard deviation σ ₂ ) as the index IN1 into the regression equation RE1.

Further, when the regression equation RE1 is represented by the above equation (Equation 3), the calculated value CV6 of the index IN4 (difference Δσ ₂₃ in standard deviation) must satisfy the correction equation CE2 represented by the following equation (Equation 9). become. In FIG. 8, the regression equation RE1 is indicated by a solid line, and the correction equations CE1 and CE2 are indicated by broken lines.

In such a case, in step S18, the correction coefficient can be updated each time track irregularity measurement data is added.

In the track irregularity prediction method of Embodiment 1, next, as shown in FIG. 17, the regression equation RE1, the calculated value CV4 of the correction coefficient CF1 calculated by the fifth calculator 15 in step S15, and the ninth calculator 19 in step S19. Based on the calculated value CV7 of the calculated correction coefficient CF2, the index IN5 representing the degree of change in the track displacement TR after the measurement unit 22 measured the track displacement TR by the seventh calculator 17 in step S17. A predicted value PV4 is calculated (step S20 in FIG. 2).

As described above, when the standard deviation of a plurality of measured values of track irregularity TR is used as index IN1, in step S20, as shown in FIG. Predict the difference. Further, if the predicted value PV4 of the index IN5 is expressed as the predicted value Δσ ₃ ^* of the difference between the third and fourth standard deviations, the predicted value PV4 of the index IN5 is expressed by the following equation (Equation 10).

Here, the leftmost factor of the right side of the above equation (Equation 10) is calculated by substituting the calculated value CV5 (standard deviation σ ₃ ) as the index IN1 into the regression equation RE1.

By performing steps S17 to S20, each time additional track irregularity data is obtained, additional predicted and measured track irregularity values are obtained, and thus the correction coefficient is updated. Repeated acquisition of track irregularity data and updating of correction coefficients makes it possible to predict track irregularity with higher accuracy.

Note that steps S17 to S20 can be repeated. That is, the measurement of the track irregularity can be repeated in the same manner as the third time, the fourth time, the fifth time, and so on (i is a natural number of 2 or more). Then, after the i-th measurement of the track irregularity, when predicting the i-th and i+1-th standard deviation difference, the predicted value Δσ _i ^* of the i-th and i+1-th standard deviation difference is given below. It is represented by the formula (Equation 11).

Here, the factor on the left side of the right side of the above equation (Equation 11) is the track deviation after the i-th measurement of the track displacement TR and before the i+1-th measurement of the track displacement TR. It represents a predicted value of an index representing the degree of change in the displacement TR. Further, the factor on the right side of the right side of the equation (Equation 11) above represents a correction coefficient and is expressed by the following equation (Equation 12).

In this way, the track irregularity measurement can be repeated four times and five times, the correction coefficient can be updated in the same manner as in the third measurement, and the predicted value can be corrected in the same manner as in the third measurement. .

By repeating steps S17 to S20, acquisition of track irregularity data and updating of correction coefficients are repeated, and it becomes possible to predict track irregularity with higher accuracy. Furthermore, by tracking changes in the correction coefficient, it is possible to extract sections in which the correction coefficient increases rapidly as sections in which the track irregularity rapidly increases.

Further, in the track irregularity prediction method using the track irregularity prediction system of Embodiment 1, when conditions such as the track structure, vehicle or driving change, the basic model are changed according to changes in conditions, and the correction coefficients are applied as they are. By predicting the effects and effects of future track irregularities on changes in track irregularities in response to changes in conditions, it is also possible to consider measures such as improving track structures, renewing rolling stock, and increasing speed. is. In addition, since changes in track irregularity in the future can be calculated even when conditions change, it is possible to evaluate measures by comparing changes in life cycle costs when measures are implemented and when they are not implemented. Therefore, it is possible to realize a track irregularity prediction method and a track irregularity prediction system for predicting future track irregularities that can cope with changes in conditions such as track structures, vehicles, and driving conditions.

<Modified Example of Track Irregularity Prediction System and Track Irregularity Prediction Method>
Next, modified examples of the track irregularity prediction system and track irregularity prediction method of Embodiment 1 will be described. In the track irregularity prediction method of this modification, instead of the correction coefficient CF1 being the ratio of the calculated value CV3 of the index IN2 to the calculated predicted value PV1 of the index IN2, It differs from the track irregularity prediction method of the first embodiment in that it is the difference between the calculated values CV3 of the index IN2.

FIG. 9 is a diagram for explaining calculation of a correction coefficient by a track irregularity prediction method according to a modification of the first embodiment. As in FIG. 5, FIG. 9 schematically shows a graph in which the horizontal axis represents the standard deviation σ of the track displacement TR and the vertical axis represents the difference Δσ between the standard deviations.

In the track irregularity prediction method using the track irregularity prediction system of this modified example, steps S11 to S14 are performed in the same manner as in the first embodiment, and then, in step S15, the fifth calculator 15 calculates the following as shown in FIG. Then, in each of the plurality of intervals SC, the predicted value PV1 of the index IN2 is calculated based on the calculated value CV1 (standard deviation σ ₁ ) of the index IN1 and the regression equation RE1, and the calculated prediction of the index IN2 A correction coefficient CF1 indicating the relationship between the value PV1 and the calculated value CV3 of the index IN2 is calculated. Preferably, in step S15, the fifth calculator 15 calculates the predicted value PV1 of the index IN2 by substituting the calculated value CV1 as the index IN1 into the regression equation RE1 in each of the plurality of sections SC.

However, in this modification, in step S15, unlike in the first embodiment, the fifth calculator 15 calculates the difference between the calculated value CV3 of the index IN2 and the calculated predicted value PV1 of the index IN2 as shown in FIG. A correction coefficient CF1 is calculated. Further, if the correction coefficient CF1 is expressed as _a correction coefficient γ1, the correction coefficient γ1 is expressed by the following equation (Equation ₁₃ ).

Here, the term on the left side of the right side of the above equation (Equation 13) represents the calculated value CV3 (difference Δσ ₁₂ of the standard deviation) of the index IN2, and the term on the right side of the above equation (Equation 13) is The term on the right side represents the predicted value PV1 of the index IN2.

Further, when the regression equation RE1 is represented by the above equation (Equation 3), the calculated value CV3 (difference Δσ ₁₂ of the standard deviation) of the index IN2 must satisfy the correction equation CE3 represented by the following equation (Equation 14). become. In FIG. 9, the regression equation RE1 is indicated by a solid line, and the correction equation CE3 is indicated by a broken line.

In this modified example, as in the first embodiment, in step S15, the correction coefficient is calculated from the predicted value of the track irregularity based on the basic model and the actual measured value of the irregular track. However, as shown in FIG. 9 for the calculation method of the correction coefficient, in this modified example, as an example of the calculation method, unlike the first embodiment, the actual track irregularity measured value is used as the track irregularity predicted value by the basic model. and the difference between the measured values of actual track irregularity.

In the track irregularity prediction method using the track irregularity prediction system of this modified example, in step S16, as in the first embodiment, the sixth calculator 16, as shown in FIG. , the calculated value CV2 of the index IN1 calculated by the second calculator 12 in step S12, the regression equation RE1, and the calculated value CV4 of the correction coefficient CF1 calculated by the fifth calculator 15 in step S15. Based on this, in step S12, the second calculator 12 calculates the predicted value PV2 of the index IN3 representing the degree of change in the track irregularity TR after the track irregularity TR is measured by the measuring unit 22 .

Also in this modification, in step S16, the fifth calculator 15 predicts the difference between the second and third standard deviations, as in the first embodiment. However, if the predicted value PV2 of the index IN3 is expressed as the predicted value Δσ ₂ ^* of the difference in the second and third standard deviations, in this modification, unlike the first embodiment, in step S16, the index IN3 The predicted value PV2 is represented by the following formula (Equation 15).

Here, the term on the left side of the right side of the above equation (Formula 15) is calculated by substituting the calculated value CV2 (standard deviation σ ₂ ) as the index IN1 into the regression equation RE1.

In the track irregularity prediction method of this modification, instead of the correction coefficient CF1 being the ratio of the calculated value CV3 of the index IN2 to the calculated predicted value PV1 of the index IN2, It can be the same as the first embodiment except that it is the difference between the calculated values CV3 of the index IN2. Therefore, even in the track irregularity prediction method using the track irregularity prediction system of the present modification, for example, even if the track irregularity history data is small and not sufficient, future track irregularity can be predicted with high accuracy. It has the same effect as the track irregularity prediction method using the track irregularity prediction system.

(Embodiment 2)
<Track irregularity prediction system and track irregularity prediction method>
Next, a track irregularity prediction system and a track irregularity prediction method according to Embodiment 2 will be described. In the track irregularity prediction method of the second embodiment, instead of the regression equation RE1 showing the relationship between the index IN1 representing the dispersion of the measured values of the track irregularity TR and the index IN2 representing the degree of change in the track irregularity TR, The track irregularity prediction method of the first embodiment uses the regression equation RE2 that indicates the relationship between the index that indicates the wheel load in each of the plurality of sections SC and the index IN2 that indicates the degree of change in the track irregularity. different from

FIG. 10 is a diagram for explaining calculation of a regression equation by the track irregularity prediction method of the second embodiment. 11 and 12 are diagrams for explaining calculation of correction coefficients by the track irregularity prediction method of the second embodiment.

In the track irregularity prediction method using the track irregularity prediction system of the second embodiment, in step S11, as in the first embodiment, the first calculator 11 (see FIG. 1) calculates the following as shown in FIG. , in each of the plurality of sections SC, the track displacement TR is measured at a plurality of different measurement positions MP along the length direction of the track RA, and the plurality of track displacements TR measured at each of the plurality of measurement positions MP , an index IN1 representing variations in the measured values of the track displacement TR is calculated. Preferably, the index IN1 can be the standard deviation or the maximum value of the measured values of the track irregularities TR, but the case where the standard deviation is used will be described below as an example.

On the other hand, in the second embodiment, in step S11, unlike in the first embodiment, the first calculator 11 calculates a , and the index IN1, a cumulative wheel load WL1 in each of the plurality of sections SC is calculated as an index representing the wheel load in each of the plurality of sections SC.

Here, when the cumulative wheel load is used as an index representing the wheel load, the wheel load per wheel acting on the track is calculated according to the following formulas (Equation 16) to the following equation (Equation 21). Calculate the cumulative wheel load by multiplying the number of wheels

First, if the cumulative wheel load WL1 is expressed as the cumulative wheel load P, the cumulative wheel load P is the wheel load P _st due to the load of the vehicle, the inertia force ΔP _sp due to elevation displacement, and the dynamic wheel load ΔP _unsp due to rail unevenness. is represented by the following formula (Equation 16).

Here, the inertial force ΔP _sp due to elevation displacement and the dynamic wheel load ΔP _unsp due to rail unevenness represent the wheel load due to the track condition with respect to the wheel load P _st due to the vehicle load.

Further, the wheel load _Pst due to the load of the vehicle on the inner rail side in the straight section, the inner rail side in the curved section, and the outer rail side in the curved section is obtained by the following equation (Equation 17), the following equation (Equation 18), and the following equation (Equation 19).

Inertia force ΔP _sp due to elevation displacement and dynamic wheel load ΔP _unsp due to rail unevenness are expressed by the following equations (Equation 20) and the following equations (Equation 21).

Variables in the above formulas (17) to (21) are variables as shown below.
W ₀ : Axle load (kN) when the vehicle is stationary
_HG ^* : Vehicle effective center of gravity height (m)
v: Traveling speed of vehicle (m/s)
V: Running speed of vehicle (km/h)
R: curve radius (m)
C: Cant (m)
G: Gauge (m)
z: vertical displacement (mm)
α: Coefficient depending on the state of the seam

As shown in the above equation (Equation 16), the cumulative wheel load WL1 (cumulative wheel load P) depends on the inertia force ΔP _sp due to the elevation displacement, and as shown in the above equation (Equation 20), the inertia force due to the elevation displacement ΔP _sp depends on the elevation displacement z, and the elevation displacement z depends on the index IN1 (standard deviation σ ₁ ). Therefore, if the cumulative wheel load WL1 (cumulative wheel load P) obtained by the _first calculator 11 measuring the track displacement TR by the measuring unit 22 in step S11 is defined as the cumulative wheel load P1, then the cumulative wheel load WL1 (cumulative The wheel load P ₁ ) depends on the calculated value CV1 (standard deviation σ ₁ ) of the index IN1.

In the second embodiment, after the first calculator 11 measures the track displacement TR by the measuring unit 22 in step S11, in step S12, the second calculator 12 (see FIG. 1) performs the same as in the first embodiment. Similarly, as shown in FIG. 4, the track displacement TR is measured again by the measurement unit 22 at a plurality of measurement positions MP in each of the plurality of sections SC, and the index IN1 is calculated again.

On the other hand, in the second embodiment, in step S12, unlike the first embodiment, in addition to the index IN1, the second calculation unit 12 uses a plurality of The accumulated wheel load WL1 in each section SC is calculated again. The calculation of the cumulative wheel load WL1 in step S12 can be the same as the calculation of the cumulative wheel load WL1 in step S11. Therefore, if the cumulative wheel load WL1 (cumulative wheel load P) obtained by measuring the track displacement TR by the _second calculating unit 12 by the measuring unit 22 in step S12 is defined as the cumulative wheel load P2, the cumulative wheel load WL1 (cumulative The wheel load P ₂ ) depends on the calculated value CV2 (standard deviation σ ₂ ) of the index IN1.

In the second embodiment, next, in step S13, as in the first embodiment, the third calculator 13 (see FIG. 1) calculates the index IN1 calculated by the first calculator 11 in step S11. In step S11, the first calculator 11 measures the track displacement TR by the measuring unit 22 based on the calculated value CV1 and the calculated value CV2 of the index IN1 calculated by the second calculator 12 in step S12. After that, in step S12, the second calculator 12 calculates an index IN2 representing the degree of change in the track displacement TR before the track displacement TR is measured by the measuring unit 22. FIG. In the second embodiment, as in the first embodiment, the difference between the calculated value CV2 of the index IN1 and the calculated value CV1 of the index IN1 can be used as the index IN2.

In the second embodiment, next, in step S14, unlike in the first embodiment, the fourth calculator 14 (see FIG. 1) uses the first calculator 11 in step S11 as shown in FIG. A wheel load calculated value CL1 of the cumulative wheel load WL1 as an index representing a plurality of wheel loads calculated in each of the plurality of sections SC, and a calculated value CL1 of the accumulated wheel load WL1 in each of the plurality of sections SC by the third calculator 13 in step S13. A regression equation RE2 representing the relationship between the cumulative wheel load WL1 and the index IN2 is calculated based on the calculated values CV3 of the plurality of indices IN2. Note that FIG. 10 schematically shows a graph in which the horizontal axis is the accumulated wheel load P and the vertical axis is the difference Δσ of the standard deviation.

As described above, when the standard deviation of _a plurality of measured values of track displacement TR is used as index IN1, as shown in FIG. A regression equation RE2 is calculated from the difference _Δσ12 .

As shown in FIG. 10, a graph with the cumulative wheel load P on the horizontal axis and the difference Δσ in the standard deviation on the vertical axis shows the wheel load calculated value CL1 of the cumulative wheel load WL1 calculated in each of the plurality of sections SC. A set of data of (cumulative wheel load P ₁ ) and calculated value CV3 of index IN2 (difference Δσ ₁₂ of standard deviation) is plotted. Then, a regression calculation is performed using the regression equation RE2 represented by the following formula (Formula 22) or the following formula (Formula 23) as an example of the following formula (Formula 22). Determine the appropriate values of c and d. With such a method, the regression equation RE2 can be calculated.

In the second embodiment, next, in step S15, unlike in the first embodiment, the fifth calculation unit 15 (see FIG. 1), as shown in FIG. Based on the wheel load calculation value CL1 (cumulative wheel load P ₁ ) of the weight WL1 and the regression equation RE2, the predicted value PV1 of the index IN2 is calculated, and the calculated predicted value PV1 of the index IN2 and the calculated value of the index IN2 are calculated. A correction coefficient CF1 indicating the relationship between the calculated value CV3 and the calculated value CV3 is calculated. Note that FIG. 11 schematically shows a graph in which the horizontal axis is the cumulative wheel load P and the vertical axis is the difference Δσ of the standard deviations, as in FIG. 10 .

As described above, when the standard deviation of the measured values of the track irregularities TR is used as the index IN1, in step S15, the fifth calculator 15 calculates the correction coefficient CF1 of the standard deviation difference _Δσ12 . Become.

Preferably, as shown in FIG. 11, in step S15, the fifth calculation unit 15 substitutes the calculated wheel load value CL1 as the accumulated wheel load WL1 into the regression equation RE2 in each of the plurality of sections SC. A predicted value PV1 of the index IN2 is calculated. Preferably, as shown in FIG. 11, in step S15, the fifth calculator 15 calculates the ratio of the calculated value CV3 of the index IN2 to the calculated predicted value PV1 of the index IN2 in each of the plurality of sections SC. A correction coefficient CF1 is calculated. Further, when the correction coefficient CF1 is expressed as _a correction coefficient γ1, the correction coefficient γ1 is expressed by the above equation (Equation 4) as in the _first embodiment.

Further, when the regression equation RE2 is represented by the above equation (Equation 23), the calculated value CV3 (the standard deviation difference Δσ ₁₂ ) of the index IN2 must satisfy the correction equation CE4 represented by the following equation (Equation 24). become. In FIG. 11, the regression equation RE2 is indicated by a solid line, and the correction equation CE4 is indicated by a broken line.

In the second embodiment, next, in step S16, unlike in the first embodiment, the sixth calculator 16 performs the second calculation in step S12 in each of a plurality of sections SC, as shown in FIG. Step In S12, the second calculator 12 calculates a predicted value PV2 of the index IN3 representing the degree of change in the track irregularity TR after the track irregularity TR is measured by the measuring unit 22. FIG.

As described above, when the standard deviation of a plurality of measured values of track irregularity TR is used as the index IN1, in step S16, the sixth calculator 16 predicts the difference between the second and third standard deviations. . Further, if the predicted value PV2 of the index IN3 is expressed as the predicted value Δσ ₂ ^* of the difference between the second and third standard deviations, the predicted value PV2 of the index IN3 is expressed by the above equation (Equation 6). Preferably, in step S16, the sixth calculation unit 16 substitutes the calculated wheel load value CL2 as the accumulated wheel load WL1 into the regression equation RE2 in each of the plurality of sections SC to obtain the predicted value of the index IN2. By calculating PV3 and multiplying the calculated predicted value PV3 of the index IN2 by the correction coefficient CF1, the predicted value PV2 of the index IN3 is calculated.

In the track irregularity prediction method of the second embodiment, instead of the regression equation RE1 showing the relationship between the index IN1 representing the dispersion of the measured values of the track irregularity TR and the index IN2 representing the degree of change in the track irregularity TR, is the same as in Embodiment 1, except that a regression equation RE2 indicating the relationship between an index representing the wheel load in each of the plurality of sections SC and an index IN2 representing the degree of change in the track displacement TR is used. can do. Therefore, even in the track irregularity prediction method using the track irregularity prediction system of the second embodiment, for example, even if the history data of track irregularity is small and insufficient, future track irregularity can be predicted with high accuracy. It has the same effect as the track irregularity prediction method using the track irregularity prediction system of form 1.

Further, in the second embodiment, instead of the regression equation RE1 indicating the relationship between the index IN1 representing the dispersion of the measured values of the track irregularity TR and the index IN2 representing the degree of change in the track irregularity TR, a plurality of Step S17 to step S20 can be performed.

That is, in the second embodiment, after the second calculation unit 12 measures the track displacement TR by the measurement unit 22 in step S12, in step S17, the seventh calculation unit 17 (see FIG. 1) performs 1, the track displacement TR is measured again at a plurality of measurement positions MP in each of the plurality of sections SC, and the index IN1 and the cumulative wheel load WL1 are calculated again.

Further, in the second embodiment, next, in step S18, the eighth calculator 18 (see FIG. 1), as shown in FIG. 7, similarly to the first embodiment, Then, based on the calculated value CV2 of the index IN1 calculated by the second calculator 12 in step S12 and the calculated value CV5 of the index IN1 calculated by the seventh calculator 17 in step S17, step S12 The change in the track displacement TR after the track displacement TR is measured by the measurement unit 22 by the second calculation unit 12 in step S17 and before the track displacement TR is measured by the measurement unit 22 by the seventh calculation unit 17 in step S17. An index IN4 representing the degree of is calculated.

As described above, when the standard deviation of a plurality of measured values of track displacement TR is used as index IN1, the difference between the second and third standard deviations is calculated in step S18. As the calculated value CV6 of the index IN4, the calculated value of the difference _Δσ23 between the second and third standard deviations can be used. Also, the difference Δσ ₂₃ between the second and third standard deviations is calculated by the above equation (Equation 7).

Further, in the second embodiment, next, in step S19, the ninth calculator 19 (see FIG. 1), as shown in FIG. , the predicted value PV3 of the index IN2, the calculated value CV6 of the index IN4 calculated by the eighth calculator 18 in step S18, and the correction coefficient CF1. Note that FIG. 12 schematically shows a graph in which the horizontal axis is the cumulative wheel load P and the vertical axis is the difference Δσ of the standard deviations, as in FIG. 10 .

As described above, when the standard deviation of the measured values of the track irregularities TR is used as the index IN1, in step S19, the ninth calculator 19 calculates the correction coefficient CF2 of the standard deviation difference _Δσ23 . Become.

Preferably, as shown in FIG. 12, in step S19, the ninth calculator 19 calculates the calculated value of the index IN4 with respect to the product of the predicted value PV3 of the index IN2 and the correction coefficient CF1 in each of the plurality of sections SC. A correction factor CF2, which is a ratio of CV6, is calculated. Further, when the correction coefficient CF2 is expressed as a correction coefficient γ2, the correction coefficient γ2 is expressed by the above equation ₍ Equation ₈ ).

Further, when the regression equation RE2 is represented by the above equation (Equation 23), the calculated value CV6 of the index IN2 (the difference Δσ ₂₃ in the standard deviation) must satisfy the correction equation CE5 represented by the following equation (Equation 25). become. In FIG. 12, the regression equation RE2 is indicated by a solid line, and the correction equations CE4 and CE5 are indicated by broken lines.

However, in step S19, unlike in the first embodiment, the cumulative wheel load WL1 is added to the regression equation RE2 as the wheel load calculated value CL2 of the cumulative wheel load WL1 calculated by the second calculator 12 in step S12 (the cumulative wheel load CL2). By substituting the weight P ₂ ), the predicted value PV3 of the index IN2 is calculated.

Further, in the second embodiment, next, in step S20, the tenth calculation unit 20 calculates in each of the plurality of sections SC by the seventh calculation unit 17 in step S17, unlike in the first embodiment. The wheel load calculated value CL3 (cumulative wheel load P ₃ ) of the accumulated wheel load WL1, the regression equation RE2, the calculated value CV4 of the correction coefficient CF1 calculated by the fifth calculator 15 in step S15, and the calculated value CV4 of the correction coefficient CF1 calculated in step S19 and the calculated value CV7 of the correction coefficient CF2 calculated by the ninth calculator 19, and the change in the track displacement TR after the track displacement TR is measured by the measurement unit 22 by the seventh calculator 17 in step S17. A predicted value PV4 of the index IN5 representing the degree of is calculated.

As described above, when the standard deviation of a plurality of track irregularity TR measurements is used as the index IN1, in step S20, the tenth calculator 20 predicts the difference between the third and fourth standard deviations. . Further, if the predicted value PV4 of the index IN5 is expressed as the predicted value Δσ ₃ ^* of the difference between the third and fourth standard deviations, the predicted value PV4 of the index IN5 is expressed by the above equation (Equation 10).

By repeating steps S17 to S20, acquisition of track irregularity data and updating of correction coefficients are repeated, and it becomes possible to predict track irregularity with higher accuracy. Therefore, in the track irregularity prediction method using the track irregularity prediction system of the second embodiment as well as the track irregularity prediction method using the track irregularity prediction system of the first embodiment, conditions such as track structure, vehicle or driving conditions It is possible to realize a track irregularity prediction method and a track irregularity prediction system for predicting future track irregularity that can also cope with changes in .

As for the calculation method of the index representing the wheel load, it is sufficient if the same calculation method can be used for each of the plurality of sections SC. A wheel load calculated by various calculation methods other than the wheel load calculation method can be used. Further, as described in the following modified example, an index representing rail pressure can be used instead of an index representing wheel load.

<Modified Example of Track Irregularity Prediction System and Track Irregularity Prediction Method>
As described above, as a modification of the track irregularity prediction system and track irregularity prediction method of the second embodiment, a case where an index representing rail pressure is used instead of an index representing wheel load will be described. The track irregularity prediction system and track irregularity prediction method of the second embodiment are identical to the track irregularity prediction system and track irregularity prediction method of the second embodiment except that the index representing the rail pressure is used instead of the index representing the wheel load. Since it can be the same as the prediction method, its detailed description and illustration are omitted.

In this modification, in step S11, as in the second embodiment, the first calculator 11 (see FIG. 1) calculates the length of the track RA in each of the plurality of sections SC as shown in FIG. Track displacement TR is measured at a plurality of measurement positions MP that are different from each other along the direction, and a plurality of track displacement TR measurements are performed based on the plurality of track displacement TR measurements measured at each of the plurality of measurement positions MP. An index IN1 representing variation in values is calculated.

On the other hand, in this modification, in step S11, unlike in the second embodiment, the first calculator 11 calculates the index In addition to IN1, the rail pressure in each of the plurality of sections SC is calculated as an index that indicates the rail pressure in each of the plurality of sections SC instead of the index that indicates the wheel load. Further, when an index representing rail pressure is used instead of the index representing wheel load, the rail pressure is calculated according to the following equations (Equation 26) to (Equation 31).

First, if the rail pressure is expressed as rail pressure P _r (kN), the rail pressure P _r is obtained from the cumulative wheel load P described in the second embodiment, the sleeper installation interval α (m), and the continuous Using the beam model on the elastic floor, it is represented by the following (Equation 26).

Also, the variable β in the above equation (Equation 26) is expressed using the following equations (Equation 27) to (Equation 30).

Variables in the above formulas (27) to (30) are variables as shown below.
k: Rail support spring coefficient per unit length (MN/m ² )
EI _x : Vertical bending stiffness of rail (MN・m ² )
D _P : Track pad spring coefficient (=110 MN/m)
D _B : Track bed spring coefficient (MN/m)
D _S : roadbed spring coefficient (MN/m)
h _B : Track bed thickness (mm)
_K30 : roadbed strength ⁽ MN/m3)
S _T : Sleeper bottom area (m ² )

When elastic sleepers are used as sleepers, instead of the rail support spring coefficient k per unit length, the rail support spring coefficient k' per unit length calculated by the following formula (Equation 31) is used. use.

Variables in the above formula (Formula 31) are variables as shown below.
D _M : Spring modulus of elastic material (MN/m)

As shown in the above formula (Equation 26), the rail pressure Pr depends on the cumulative wheel load P. As described in the second embodiment, the cumulative wheel load P is the calculated value _CV1 ( depends on the standard deviation σ ₁ ). Therefore, if the rail pressure _{Pr obtained by the first calculation unit 11 measuring the track displacement TR by the measurement unit 22 in step S11 is defined as the rail pressure Pr1, the rail pressure Pr1 is the} calculated value CV1 ( depends on the standard deviation σ ₁ ).

In this modification, after the first calculator 11 measures the track displacement TR by the measuring unit 22 in step S11, in step S12, the second calculator 12 (see FIG. 1) performs 4, the track displacement TR is measured again at a plurality of measurement positions MP in each of the plurality of sections SC by the measuring unit 22, and the index IN1 is calculated again.

On the other hand, in the present modification, in step S12, unlike the second embodiment, in addition to the index IN1, instead of the index representing the wheel load, the second calculation unit 12 calculates the rail pressure in each of the plurality of sections SC. As an index representing , the rail pressure _Pr in each of the plurality of sections SC is recalculated. The calculation of the rail pressure _Pr in step S12 can be the same as the calculation of the rail pressure _Pr in step S11. Therefore, if the rail pressure _{Pr obtained by the second calculation unit 12 measuring the track displacement TR by the measurement unit 22 in step S12 is defined as the rail pressure Pr2, the rail pressure Pr2 is the} calculated value CV2 ( standard deviation σ ₂ ).

In this modification, next, in step S13, in the same manner as in the second embodiment, the third calculator (see FIG. 1) calculates the track displacement TR by the first calculator 11 using the measuring unit 22 in step S11. After the measurement and before the second calculation unit 12 measures the track displacement TR by the measurement unit 22 in step S12, an index IN2 representing the degree of change in the track displacement TR is calculated.

In this modification, next, in step S14, instead of using the index representing the wheel load, the index representing the rail pressure can be used in the same manner as in the second embodiment. Then, in step S14, the fourth calculator 14 (see FIG. 1) calculates the rail pressure as an index representing the plurality of rail pressures calculated in each of the plurality of sections SC by the first calculator 11 in step S11. The index calculation value of Pr (corresponding to the wheel load calculation value CL1 in FIG. 10), and the calculation values _CV3 of the plurality of indices IN2 calculated in each of the plurality of sections SC by the third calculation unit 13 in step S13. , a regression equation RE2 representing the relationship between the index representing the rail pressure and the index IN2 is calculated (see FIG. 10).

In this modification, next, in step S15, instead of using the index representing the wheel load, the index representing the rail pressure can be used in the same manner as in the second embodiment. Then, in step S15, the fifth calculator 15 (see FIG. 1) calculates the index calculated value of the rail pressure _Pr (corresponding to the wheel load calculated value CL1 in FIG. 11) and the regression equation A predicted value PV1 of the index IN2 is calculated based on RE2, and a correction coefficient CF1 indicating the relationship between the calculated predicted value PV1 of the index IN2 and the calculated value CV3 of the index IN2 is calculated (see FIG. 11). ).

In this modification, next, in step S16, instead of using the index representing the wheel load, the index representing the rail pressure can be used in the same manner as in the second embodiment. Then, in step S16, the sixth calculator 16 (see FIG. 1) calculates the index calculated value of the rail pressure _Pr calculated by the second calculator 12 in step S12 (see FIG. 11) in each of the plurality of sections SC. ), the regression equation RE2, and the calculated value CV4 of the correction coefficient CF1 calculated by the fifth calculator 15 in step S15, the second calculator in step S12 12 calculates a predicted value PV2 of the index IN3 representing the degree of change in the track irregularity TR after the track irregularity TR is measured by the measuring unit 22 (see FIG. 11).

The track irregularity prediction method of this modification can be the same as that of the second embodiment, except that the index representing the rail pressure is used instead of the index representing the wheel load. Therefore, even in the track irregularity prediction method using the track irregularity prediction system of this modified example, even if the history data of track irregularity is small and insufficient, future track irregularity can be predicted with high accuracy. It has the same effect as the track irregularity prediction method using the track irregularity prediction system.

In this modification, steps S17 to S20 can be performed in the same manner as in the second embodiment, except that the index representing the rail pressure is used instead of the index representing the wheel load (see FIG. 12). .

By repeating steps S17 to S20, acquisition of track irregularity data and updating of correction coefficients are repeated, and it becomes possible to predict track irregularity with higher accuracy. Therefore, even in the track irregularity prediction method using the track irregularity prediction system of this modified example, as in the track irregularity prediction method using the track irregularity prediction system of the second embodiment, changes in conditions such as track structure, vehicles, or driving conditions It is possible to realize a track irregularity prediction method and a track irregularity prediction system for predicting future track irregularity that can also cope with

Although the invention made by the present inventor has been specifically described based on the embodiment, the invention is not limited to the above embodiment, and can be variously modified without departing from the gist of the invention. Needless to say.

Within the scope of the idea of the present invention, those skilled in the art can conceive of various modifications and modifications, and it is understood that these modifications and modifications also fall within the scope of the present invention.

For example, a person skilled in the art may appropriately add, delete, or change the design of components, or add, omit, or change the conditions of the above-described embodiments. As long as it has the gist, it is included in the scope of the present invention.

INDUSTRIAL APPLICABILITY The present invention is effective when applied to a track irregularity prediction method and a track irregularity prediction system for predicting track irregularity.

10 Track irregularity prediction system 11 First calculator 12 Second calculator 13 Third calculator 14 Fourth calculator 15 Fifth calculator 16 Sixth calculator 17 Seventh calculator 18 Eighth calculator 19 Ninth calculator 20 Tenth calculator 21 Calculator 22 Measurement unit 23 Control unit CE1-CE5 Correction formulas CF1, CF2 Correction coefficients CL1-CL3 Wheel load calculated values CV1-CV7 Calculated values IN1-IN5 Index MP Measurement positions PV1-PV4 Predicted value RA Track RE1, RE2 Regression formula SC, SC1 to SC5 Section TR, TR1 to TR3 Track irregularity WL1 Cumulative wheel load

Claims (13)

A track displacement prediction method for predicting track displacement in each of the plurality of sections of the track including the plurality of sections arranged along the length of the track,
(a) in each of the plurality of sections, measuring the track displacement at a plurality of different measurement positions along the length direction, and measuring the track displacement measured at each of the plurality of measurement positions; a step of calculating a first index representing variation in measured values;
(b) after measuring the track irregularity in step (a), re-measuring the track irregularity at the plurality of measurement positions in each of the plurality of sections, and re-calculating the first index;
(c) in each of the plurality of sections, a first calculated value of the first index calculated in step (a) and a second calculation of the first index calculated in step (b); calculating a second index representing the degree of change in the track irregularity before the track irregularity is measured in step (b), based on the value;
(d) the plurality of first calculated values respectively calculated in each of the plurality of sections, and the third of the plurality of second indices calculated in each of the plurality of sections in step (c); calculating a regression expression representing the relationship between the first index and the second index based on the calculated value;
(e) calculating a first predicted value of the second index based on the first calculated value and the regression equation in each of the plurality of intervals, and calculating the calculated first predicted value; a step of calculating a first correction coefficient indicating the relationship between the third calculated value and
(f) in each of the plurality of intervals, based on the second calculated value, the regression equation, and the fourth calculated value of the first correction coefficient calculated in step (e), (b) calculating a second predicted value of a third index representing the degree of change in the track irregularity after the track irregularity is measured in step;
A track irregularity prediction method. In the track irregularity prediction method according to claim 1,
In the step (e), in each of the plurality of intervals, the first predicted value is calculated by substituting the first calculated value as the first index into the regression equation, and the calculated first A track irregularity prediction method, wherein the first correction coefficient, which is a ratio of the third calculated value to the predicted value, is calculated. In the track irregularity prediction method according to claim 2,
In the step (f), in each of the plurality of intervals, by substituting the second calculated value as the first index into the regression equation, a third predicted value of the second index is calculated, and and calculating the second predicted value by multiplying the third predicted value obtained by multiplying the third predicted value by the first correction coefficient. In the track irregularity prediction method according to claim 3,
(g) after measuring the track irregularity in step (b), re-measuring the track irregularity at the plurality of measurement positions in each of the plurality of sections, and re-calculating the first index;
(h) in each of the plurality of sections, based on the second calculated value and the fifth calculated value of the first index calculated in step (g), in step (g) calculating a fourth index representing the degree of change in the track irregularity before measuring the track irregularity;
(i) showing the relationship between the third predicted value, the sixth calculated value of the fourth index calculated in step (h), and the first correction coefficient in each of the plurality of intervals; calculating a second correction factor;
(j) in each of the plurality of intervals, the fifth calculated value, the regression equation, the fourth calculated value, and the seventh calculated value of the second correction coefficient calculated in step (i); and calculating a fourth predicted value of a fifth index representing the degree of change in the track irregularity after the track irregularity is measured in step (g),
A track irregularity prediction method. In the track irregularity prediction method according to claim 4,
In step (i), in each of the plurality of intervals, calculating the second correction coefficient, which is the ratio of the sixth calculated value to the product of the third predicted value and the first correction coefficient; Track irregularity prediction method. In the track irregularity prediction method according to claim 1,
In the step (e), in each of the plurality of intervals, the first predicted value is calculated by substituting the first calculated value as the first index into the regression equation, and the calculated first A track irregularity prediction method, wherein the first correction coefficient, which is a difference between the third calculated value and the predicted value, is calculated. A track displacement prediction method for predicting track displacement in each of the plurality of sections of the track including the plurality of sections arranged along the length of the track,
(a) in each of the plurality of sections, measuring the track displacement at a plurality of different measurement positions along the length direction, and measuring the track displacement measured at each of the plurality of measurement positions; a step of calculating, based on the measured values, a first index representing variations in the measured values of the plurality of track irregularities and a second index representing the wheel load or rail pressure in each of the plurality of sections;
(b) after measuring the track irregularity in step (a), measure the track irregularity again at the plurality of measurement positions in each of the plurality of sections; recalculating the
(c) in each of the plurality of sections, the first calculated value of the first index calculated in step (a) and the second calculation of the first index calculated in step (b); a step of calculating a third index representing the degree of change in the track irregularity before the track irregularity is measured in step (b), based on the value;
(d) first index calculated values of the plurality of second indices calculated in each of the plurality of sections in step (a); and each of the plurality of sections in step (c); calculating a regression equation indicating the relationship between the second index and the third index based on the calculated third calculated values of the plurality of the third indices;
(e) in each of the plurality of intervals, calculating a first predicted value of the third index based on the calculated first index value and the regression equation; , the step of calculating a first correction coefficient indicating the relationship between the third calculated value and
(f) in each of the plurality of sections, the second index calculated value of the second index calculated in step (b), the regression equation, and the second index calculated in step (e); calculating a second predicted value of a fourth index representing the degree of change in the track irregularity after the track irregularity is measured in step (b), based on the fourth calculated value of the 1 correction coefficient; ,
A track irregularity prediction method. A track displacement prediction system that predicts track displacement in each of the plurality of sections of the track including the plurality of sections arranged along the length of the track,
In each of the plurality of sections, the track displacement is measured at a plurality of measurement positions different from each other along the length direction, and each of the plurality of track displacement measurement values measured at each of the plurality of measurement positions is measured. a first calculation unit that calculates a first index representing variation;
a second calculator for re-measuring the track irregularity at the plurality of measurement positions in each of the plurality of sections after the track irregularity is measured by the first calculator and re-calculating the first index; ,
Based on the first calculated value of the first index calculated by the first calculator and the second calculated value of the first index calculated by the second calculator in each of the plurality of sections a third calculator that calculates a second index representing a degree of change in the track irregularity before the track irregularity is measured by the second calculator;
a plurality of first calculated values respectively calculated in each of the plurality of sections; and a third calculated value of the plurality of second indices calculated in each of the plurality of sections by the third calculation unit; a fourth calculation unit that calculates a regression expression representing the relationship between the first index and the second index based on
In each of the plurality of intervals, a first predicted value of the second index is calculated based on the first calculated value and the regression equation, and the calculated first predicted value and the third A fifth calculation unit that calculates a first correction coefficient that indicates the relationship between the calculated value and
In each of the plurality of intervals, the second calculation unit based on the second calculation value, the regression equation, and the fourth calculation value of the first correction coefficient calculated by the fifth calculation unit a sixth calculator that calculates a second predicted value of a third index representing the degree of change in the track irregularity after the track irregularity is measured by
A track displacement prediction system. In the track irregularity prediction system according to claim 8,
The fifth calculation unit calculates the first predicted value by substituting the first calculated value as the first index into the regression equation in each of the plurality of intervals, and calculates the calculated first predicted value. A track irregularity prediction system that calculates the first correction coefficient that is the ratio of the third calculated value to the predicted value. In the track irregularity prediction system according to claim 9,
The sixth calculation unit calculates a third predicted value of the second index by substituting the second calculated value as the first index into the regression equation in each of the plurality of intervals, and A track irregularity prediction system that calculates the second predicted value by multiplying the third predicted value obtained by multiplying the third predicted value by the first correction coefficient. In the track irregularity prediction system according to claim 10,
a seventh calculator for re-measuring the track irregularity at the plurality of measurement positions in each of the plurality of sections after the track irregularity is measured by the second calculator and recalculating the first index; ,
In each of the plurality of sections, the track displacement is measured by the seventh calculator based on the second calculated value and the fifth calculated value of the first index calculated by the seventh calculator. an eighth calculator that calculates a fourth index representing the degree of change in the track irregularity before the
a second correction coefficient representing a relationship between the third predicted value, the sixth calculated value of the fourth index calculated by the eighth calculator, and the first correction coefficient in each of the plurality of intervals; a ninth calculator that calculates
Based on the fifth calculated value, the regression equation, the fourth calculated value, and the seventh calculated value of the second correction coefficient calculated by the ninth calculator in each of the plurality of intervals a tenth calculator for calculating a fourth predicted value of a fifth index representing a degree of change in the track irregularity after the track irregularity is measured by the seventh calculator;
A track displacement prediction system. In the track irregularity prediction system according to claim 11,
The ninth calculator calculates the second correction coefficient, which is a ratio of the sixth calculated value to the product of the third predicted value and the first correction coefficient, in each of the plurality of intervals. Track irregularity prediction system. In the track irregularity prediction system according to claim 8,
The fifth calculation unit calculates the first predicted value by substituting the first calculated value as the first index into the regression equation in each of the plurality of intervals, and calculates the calculated first predicted value. A track irregularity prediction system that calculates the first correction coefficient that is the difference between the third calculated value and the predicted value.

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